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Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting.

, , , , , , , and . HPCA, page 271-282. IEEE Computer Society, (2015)

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Input responsiveness: using canary inputs to dynamically steer approximation., , , , , and . PLDI, page 161-176. ACM, (2016)Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting., , , , , , , and . HPCA, page 271-282. IEEE Computer Society, (2015)Proctor: Detecting and Investigating Interference in Shared Datacenters., , , , and . ISPASS, page 76-86. IEEE Computer Society, (2018)CrystalBall: Statically analyzing runtime behavior via deep sequence learning., , , , , and . MICRO, page 24:1-24:12. IEEE Computer Society, (2016)Architectural support for convolutional neural networks on modern CPUs., , , , and . PACT, page 16:1-16:13. ACM, (2018)SMiTe: Precise QoS Prediction on Real-System SMT Processors to Improve Utilization in Warehouse Scale Computers., , , and . MICRO, page 406-418. IEEE Computer Society, (2014)Continuous shape shifting: Enabling loop co-optimization via near-free dynamic code rewriting., , , and . MICRO, page 23:1-23:12. IEEE Computer Society, (2016)AREP: Adaptive Resource Efficient Prefetching for Maximizing Multicore Performance., , , , and . PACT, page 367-378. IEEE Computer Society, (2015)An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction., , , , , , , , , and 1 other author(s). EMNLP/IJCNLP (1), page 1311-1316. Association for Computational Linguistics, (2019)DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission., , , , , , , , and . MICRO, page 786-799. ACM, (2017)